Automated Segmentation of Graft Material in 1‐Stage Sinus Lift Based on Artificial Intelligence: A Retrospective Study

Abstract

ObjectivesAccurate assessment of postoperative bone graft material changes after the 1‐stage sinus lift is crucial for evaluating long‐term implant survival. However, traditional manual labeling and segmentation of cone‐beam computed tomography (CBCT) images are often inaccurate and inefficient. This study aims to utilize artificial intelligence for automated segmentation of graft material in 1‐stage sinus lift procedures to enhance accuracy and efficiency.Materials and MethodsSwin‐UPerNet along with mainstream medical segmentation models, such as FCN, U‐Net, DeepLabV3, SegFormer, and UPerNet, were trained using a dataset of 120 CBCT scans. The models were tested on 30 CBCT scans to evaluate model performance based on metrics including the 95% Hausdorff distance, Intersection over Union (IoU), and Dice similarity coefficient. Additionally, processing times were also compared between automated segmentation and manual methods.ResultsSwin‐UPerNet outperformed other models in accuracy, achieving an accuracy rate of 0.84 and mean precision and IoU values of 0.8574 and 0.7373, respectively (p < 0.05). The time required for uploading and visualizing segmentation results with Swin‐UPerNet significantly decreased to 19.28 s from the average manual segmentation times of 1390 s (p < 0.001).ConclusionsSwin‐UPerNet exhibited high accuracy and efficiency in identifying and segmenting the three‐dimensional volume of bone graft material, indicating significant potential for evaluating the stability of bone graft material.